基于情感分析的循环神经网络股票波动率预测
Stock Volatility Prediction Using Recurrent Neural Networks with Sentiment Analysis
摘要：
本文提出了一个分析网上股票论坛情绪的模型，并利用该信息对中国股市的波动性进行预测。我们已经标记了在线金融帖子的情绪，并将数据集公开用于研究。通过生成基于金融术语的情感词典，我们开发了一个模型来计算与特定股票相关的每个在线帖子的情感得分。这种情绪信息由两个情绪指标表示，通过使用循环神经网络（RNNs）将其融合到市场数据中用于股票波动预测。实证研究表明，与仅使用RNN相比，该模型在情感指标上的表现明显更好。
英文摘要：
In this paper, we propose a model to analyze sentiment of online stock forum and use the information to predict the stock volatility in the Chinese market. We have labeled the sentiment of the online financial posts and make the dataset public available for research. By generating a sentimental dictionary based on financial terms, we develop a model to compute the sentimental score of each online post related to a particular stock. Such sentimental information is represented by two sentiment indicators, which are fused to market data for stock volatility prediction by using the Recurrent Neural Networks (RNNs). Empirical study shows that, comparing to using RNN only, the model performs significantly better with sentimental indicators.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
